Dataset Information

Comparing Emergency Department Use Among Medicaid and Commercial Patients Using All-Payer All-Claims Data.

ABSTRACT: The high rate of emergency department (ED) use by Medicaid patients is not fully understood. The objective of this paper is (1) to provide context for ED service use by comparing Medicaid and commercial patients' differences across ED and non-ED health service use, and (2) to assess the extent to which Medicaid-commercial differences in ED use can be explained by observable factors in administrative data. Statistical decomposition methods were applied to ED, mental health, and inpatient care using 2011-2013 Medicaid and commercial insurance claims from the Oregon All Payer All Claims database. Demographics, comorbidities, health services use, and neighborhood characteristics accounted for 44% of the Medicaid-commercial difference in ED use, compared to 83% for mental health care and 75% for inpatient care. This suggests that relative to mental health and inpatient care, a large portion of ED use cannot be explained by administrative data. Models that further accounted for patient access to different primary care physicians explained an additional 8% of the Medicaid-commercial difference in ED use, suggesting that the quality of primary care may influence ED use. The remaining unexplained difference suggests that appropriately reducing ED use remains a credible target for policy makers, although success may require knowledge about patients' perceptions and behaviors as well as social determinants of health.

SUBMITTER: Kim H

PROVIDER: S-EPMC5564052 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comparing Emergency Department Use Among Medicaid and Commercial Patients Using All-Payer All-Claims Data.

Kim Hyunjee H McConnell K John KJ Sun Benjamin C BC

Population health management 20170111 4

The high rate of emergency department (ED) use by Medicaid patients is not fully understood. The objective of this paper is (1) to provide context for ED service use by comparing Medicaid and commercial patients' differences across ED and non-ED health service use, and (2) to assess the extent to which Medicaid-commercial differences in ED use can be explained by observable factors in administrative data. Statistical decomposition methods were applied to ED, mental health, and inpatient care usi ...[more]

PMID: 28075692

Similar Datasets

Project description:BackgroundAsthma exacerbation leading to emergency department (ED) visit is prevalent, an indicator of poor control of asthma, and is a potentially preventable clinical outcome.ObjectiveWe propose to utilize multiple data elements available in electronic medical records (EMRs) and claims database to create separate algorithms with high validity for clinical and research purposes to identify asthma exacerbation-related ED visit among the general population.MethodsWe performed a retrospective study with inclusion criteria of patients aged 4 to 40 years, a visit to Geisinger ED from January 1, 2006, to October 28, 2013, with asthma on their problem list. Different electronic data elements including chief complaints, vitals, season, smoking, medication use, and discharge diagnoses were obtained to create the algorithm. A stratified random sample was generated to select the charts for review. Chart review was performed to classify patients with asthma-related ED visit, that is, the gold standard. Two reviewers performed the chart review and validation was done on a small subset.ResultsThere were 966 eligible ED visits in the EMR sample and 731 in the claims sample. Agreement between reviewers was 95.45% and kappa statistic was 0.91. Mean age of the EMR sample was 22 years, and mostly white (93%). Multiple models conventionally used in studies were evaluated and the final model chosen included principal diagnosis, bronchodilator, and steroid use for both algorithms, chief complaints for EMR, and secondary diagnosis for claims. Area under the curve was 0.93 (95% confidence interval: 0.91-0.94) and 0.94 (0.93-0.96), respectively, for EMR and claims data, with positive predictive value of?>?94%. The algorithms are visually presented using nomograms.ConclusionWe were able to develop two separate algorithms for EMR and claims to identify asthma exacerbation-related ED visit with excellent diagnostic ability and varying discrimination threshold for clinical and research purposes.

Dataset Information

Comparing Emergency Department Use Among Medicaid and Commercial Patients Using All-Payer All-Claims Data.

Publications

Comparing Emergency Department Use Among Medicaid and Commercial Patients Using All-Payer All-Claims Data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure